Biometric authentication based on perceptual resonance between CNN emergent patterns and humans

This paper investigates a novel application in biometry of the emergent complex patterns in cellular neural networks. Using a set of patterns generated in the "unstable" regime of discrete-time generalized cellular automata (GCA), it was found that human subjects exposed to these patterns would resonate perceptually with them in a personal manner which qualifies such a resonance as a biometric "brain signature" of a person. Further research indicates that by using an large enough set of stimuli, a simple linear perceptron can discriminate among persons, thus performing authentication at a low implementation cost.